A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

Markdown - это облегчённый язык разметки, который преобразует текст в структурированный HTML. Следующее руководство поможет вам разобраться, как использовать Markdown.

# Заголовок первого уровня
## Заголовок второго уровня
### Заголовок третьего уровня
#### Заголовок четвёртого уровня
##### Заголовок пятого уровня

Purpose & scope. A defensive/educational knowledge base cataloguing known prompt-injection and jailbreak patterns, the models/systems they have affected, and the defenses against them. Compiled from primary literature (arXiv papers, vendor disclosures) and security research, June 2026.

How to read this. Every technique lists: how it works, an illustrative structural skeleton (the shape of the attack, not a weaponized payload), the models/systems it was reported against, and its current status. Examples are deliberately defanged. >

This is the future

It works on devices with linux kernel version over 5.10. Right now, the procedure is to get pre-built binaries of the kernel packaged in the boot.img.

From their page:

KernelSU is a root solution for Android GKI devices, it works in kernel mode and grant root permission to userspace application directly in kernel space.